EPFL
 Biomedical Imaging GroupSTI
EPFL
  Publications
English only   BIG > Publications > Bayesian Denoising


 CONTENTS
 Home Page
 News & Events
 People
 Publications
 Tutorials and Reviews
 Research
 Demos
 Download Algorithms

 DOWNLOAD
 PDF
 Postscript
 All BibTeX References

Bayesian Denoising of Generalized Poisson Processes with Finite Rate of Innovation

A. Amini, U. Kamilov, M. Unser

Proceedings of the Thirty-Seventh IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'12), 京都市 (Kyoto), Japan, March 25-30, 2012, pp. 3629-3632.



We investigate the problem of the optimal reconstruction of a generalized Poisson process from its noisy samples. The process is known to have a finite rate of innovation since it is generated by a random stream of Diracs with a finite average number of impulses per unit interval. We formulate the recovery problem in a Bayesian framework and explicitly derive the joint probability density function (pdf) of the sampled signal. We compare the performance of the optimal Minimum Mean Square Error (MMSE) estimator with common regularization techniques such as ℓ1 and Log penalty functions. The simulation results indicate that, under certain conditions, the regularization techniques can achieve a performance close to the MMSE method.


@INPROCEEDINGS(http://bigwww.epfl.ch/publications/amini1202.html,
AUTHOR="Amini, A. and Kamilov, U. and Unser, M.",
TITLE="{B}ayesian Denoising of Generalized {P}oisson Processes with
        Finite Rate of Innovation",
BOOKTITLE="Proceedings of the Thirty-Seventh {IEEE} International
        Conference on Acoustics, Speech, and Signal Processing
        ({ICASSP'12})",
YEAR="2012",
editor="",
volume="",
series="",
pages="3629--3632",
address="Kyoto, Japan",
month="March 25-30,",
organization="",
publisher="",
note="")

© 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.